Method of detecting noise in auscultatory sound signals of a coronary-artery-disease detection system

ABSTRACT

A time-series array of noise data is generated from an inverse frequency transform of the product of the frequency spectrum of an auscultatory sound signal with an associated noise filter generated responsive to a cross-correlation of frequency spectra of auscultatory sound signals from adjacent auscultatory sound-or-vibration sensors on the torso of a test subject. Noise power within at least one range of frequencies of average of frequency spectra from a plurality of windows of the time-series array of noise data is compared with a threshold to determine whether or not the associated auscultatory sound-or-vibration sensor is excessively noisy. In one embodiment, the noise filter is generated by subtracting from unity, a unity-normalized cross-correlation of frequency spectra of the auscultatory sound signals, wherein the resulting values are clipped so as to be no less than an associated noise floor.

CROSS-REFERENCE TO RELATED APPLICATIONS

The instant application is a continuation-in-part of U.S. application Ser. No. 16/136,015 filed on 19 Sep. 2018, which claims the benefit of the following: U.S. Provisional Application Ser. No. 62/560,568 filed on 19 Sep. 2017, U.S. Provisional Application Ser. No. 62/568,155 filed on 4 Oct. 2017, U.S. Provisional Application Ser. No. 62/575,364 filed on 20 Oct. 2017, and U.S. Provisional Application Ser. No. 62/575,383 filed on 21 Oct. 2017, each of which is incorporated herein by reference in its entirety.

The instant application directly claims benefit of U.S. Provisional Application Ser. No. 62/575,383 filed on 21 Oct. 2017, which is incorporated herein by reference in its entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 illustrates a block diagram of a coronary-artery-disease detection system;

FIG. 2 illustrates a first aspect of a data recording module and a first aspect of an associated docking system, in accordance with a first aspect of the coronary-artery-disease detection system illustrated in FIG. 1;

FIG. 3 illustrates a fragmentary view of a human thorax and associated prospective locations of auscultatory sound sensors associated right, sternum and left, second, third, fourth and fifth, inter-costal spaces, left posterior locations at the second and third inter-costal spaces, and locations proximate to the heart apex;

FIG. 4 illustrates a second aspect of a data recording module and a second aspect of an associated docking system, in accordance with a second aspect of the coronary-artery-disease detection system illustrated in FIG. 1;

FIG. 5a illustrates an auscultatory sound sensor coupled to the skin of a test-subject, by bonding via associated adhesive layers or surfaces on both sides of an adhesive interface;

FIGS. 5b and 5c each illustrate an auscultatory sound sensor that is detached, and therefore fully decoupled, from the skin of a test-subject, wherein FIG. 5b illustrates the associated adhesive interface detached from the skin of the test-subject, and FIG. 5c illustrates the associated adhesive interface detached from the auscultatory sound sensor;

FIGS. 5d through 5g each illustrate an auscultatory sound sensor that is partially coupled to, but debonded from, the skin of a test-subject;

FIG. 6 illustrates a test-subject reclined on a surface, with their torso inclined while capturing auscultatory sound signals from a plurality of auscultatory sound sensors attached to the thorax of the test-subject;

FIG. 7 illustrates a flowchart of a first aspect of an associated auscultatory-sound-sensing process that incorporates a process for detecting a decoupling of the associated auscultatory sound sensors from the skin of the thorax of a test-subject being diagnosed for a prospective abnormal cardiovascular condition, wherein the decoupling-detection process occurs after each block of breath-held auscultatory sound time-series data is acquired, and is based upon scaled time-series data and responsive to an associated pre-determined debond-detection threshold;

FIG. 8 illustrates a flowchart of a first aspect of a process for acquiring auscultatory sound signals from the associated auscultatory sound sensors coupled to the skin of the thorax of the test-subject being diagnosed for a prospective abnormal cardiovascular condition;

FIG. 9 illustrates a plurality of six blocks of breath-held, auscultatory-sound-sensor time-series data recorded from an auscultatory sound sensor coupled to the skin of the thorax of a test-subject being diagnosed for a prospective abnormal cardiovascular condition;

FIGS. 10a-10f respectively illustrate a simulation of successively recorded blocks of breath-held, sensor time-series data illustrated in FIG. 9, each illustrated with an expanded time scale, wherein FIGS. 10a-10e illustrates a condition for which the auscultatory sound sensor is coupled to the skin of the test-subject, and FIG. 10f illustrates a condition for which the auscultatory sound sensor is decoupled from the skin of the test-subject;

FIG. 11 illustrates a flowchart of a process for determining a scale factor used to scale auscultatory-sound-sensor time-series data, the latter of which is analyzed to detect whether or not the associated auscultatory sound sensor is decoupled from the skin of the test-subject, wherein the scale factor provides for directly determining if the associated auscultatory sound sensor is detached from the skin of the test-subject;

FIGS. 12a-12f respectively illustrate time-series of the absolute values of the corresponding time-series data illustrated in FIGS. 10a-10f , further illustrating a division of the block of breath-held, sensor time-series data into a plurality of associated data segments, with each data segment of sufficient width to nominally include sound from a single heartbeat, and with the peak values in each data segment marked, wherein FIGS. 12a-12e illustrates a condition for which the auscultatory sound sensor is coupled to the skin of the test-subject, and FIG. 12f illustrates a condition for which the auscultatory sound sensor is decoupled from the skin of the test-subject;

FIG. 13 illustrates an accelerometer on the thorax of a test-subject during a respiration cycle of the test-subject;

FIG. 14 illustrates a breath-hold detection process;

FIG. 15 illustrates a flowchart of a first aspect of a process for detecting whether or not an auscultatory sound sensor is debonded from the skin of a test-subject;

FIG. 16 illustrates an organization of data from an auscultatory sound sensor recorded by an auscultatory coronary-artery-disease detection system from a test subject;

FIG. 17 illustrates a flowchart of a noise detection process;

FIG. 18 illustrates a flowchart of a process for generating a matched noise filter;

FIG. 19 illustrates a flowchart of a process for evaluating the noise content in a spectral signal of an auscultatory sound signal; and

FIG. 20 illustrates a flowchart of a process for logging results from the noise-evaluation process of FIG. 19.

DESCRIPTION OF EMBODIMENT(S)

Referring to FIGS. 1 and 2, an auscultatory coronary-artery-disease detection system 10 incorporates at least one auscultatory sound sensor 12 that is operatively coupled to a recording module 13 running at least a first portion 14.1 of a Data Recording Application (DRA) 14, 14.1 on a first specialized computer or electronic system comprising a first computer processor or FPGA (Field Programmable Gate Array) 15 and first memory 17 powered by a first power supply 19, which provides for recording and preprocessing auscultatory sound signals 16 from the at least one auscultatory sound sensor 12. For example, in one set of embodiments, the at least one auscultatory sound sensor 12 comprises a first group 12′ of three auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′) physically interconnected end-to-end with one another, and physically and electrically interconnected with a first wireless transceiver 18; and a second group 12″ of three auscultatory sound sensors 12, 12 ^(1″), 12 ^(2″), 12 ^(3″) physically interconnected end-to-end with one another, and physically and electrically interconnected with the first wireless transceiver 18, with both groups 12′, 12″ of auscultatory sound sensors placed on the skin of the thorax 20 of a test-subject 22, in acoustic communication therewith. Referring also to FIG. 3, the placement of the first group of auscultatory sound sensors 12′ in FIG. 1 is illustrated with the respective associated auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′) in substantial alignment with the corresponding respective third R3, fourth R4 and fifth R5, inter-costal spaces on the right side 20 ^(R) of the thorax 20, and the placement of the second group of auscultatory sound sensors 12″ in FIG. 1 is illustrated with the respective associated auscultatory sound sensors 12, 12 ^(1″), 12 ^(2″), 12 ^(3″) in substantial alignment with the corresponding respective third L3, fourth L4 and fifth L5, inter-costal spaces on the left side 20 ^(L) of the thorax 20. Prospective sensor locations R2-R5, S2-S5, and L2-L5 illustrated in FIG. 3 respectively refer to the second R2 through fifth R5 inter-costal spaces on the right side 20 ^(R) of the thorax 20, the second S2 through fifth S5 inter-costal spaces at the center/sternum 20 ^(S) of the thorax 20, and the second L2 through fifth L5 inter-costal spaces on the left side 20 ^(L) of the thorax 20. Furthermore, prospective left-side posterior sensor locations LP2 and LP3 illustrated in FIG. 3 respectively refer to at the second LP2 and third LP3 intercostal spaces locations on the posterior of the thorax 20. Yet further, prospective sensor locations HA-1 and HA-2 are proximate to the apex of the heart, either on the anterior or the posterior of the thorax 20. For example, in one set of embodiments, The auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3″), 12 ^(1″), 12 ^(2″), 12 ^(3″) are located at the second S2, L2, third S3, L3 and fourth S4, L4 inter-costal spaces at the sternum S2-S4 and left-side L2-L4 of the thorax 20.

As used herein, the term “auscultatory sound” is intended to mean a sound originating from inside a human or animal organism as a result of the biological functioning thereof, for example, as might be generated by action of the heart, lungs, other organs, or the associated vascular system; and is not intended to be limited to a particular range of frequencies—for example, not limited to a range of frequencies or sound intensities that would be audible to a human ear,—but could include frequencies above, below, and in the audible range, and sound intensities that are too faint to be audible to a human ear. Furthermore, the term “auscultatory-sound sensor” is intended to mean a sound sensor that provides for transducing auscultatory sounds into a corresponding electrical or optical signal that can be subsequently processed.

The auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) provide for transducing the associated sounds received thereby into corresponding auscultatory sound signals 16 that are preprocessed and recorded by an associated hardware-based signal conditioning/preprocessing and recording subsystem 25, then communicated to the first wireless transceiver 18, and then wirelessly transmitted thereby to an associated second wireless transceiver 26 of an associated wireless interface 26′ of an associated docking system 27, possibly running a second portion 14.2 of the Data Recording Application (DRA) 14, 14.2 on a corresponding second specialized computer or electronic system comprising an associated second computer processor or FPGA (Field Programmable Gate Array) 28 and second memory 30, both of which are powered by an associated second power supply 32, which together provide for recording and preprocessing the associated auscultatory sound signals 16 from the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″).

For example, in accordance with one set of embodiments, the hardware-based signal conditioning/preprocessing and recording subsystem 25 includes an amplifier—either of fixed or programmable gain,—a filter and an analog-to-digital converter (ADC). For example, in one set of embodiments, the analog-to-digital converter (ADC) is a 16-bit analog-to-digital converter (ADC) that converts a −2.25 to +2.25 volt input to a corresponding digital value of −32, 768 to +32, 767. Furthermore, in accordance with one set of embodiments of the amplifier, the amplifier gain is programmable to one of sixteen different levels respectively identified as levels 0 to 15, with corresponding, respective gain values of 88, 249, 411, 571, 733, 894, 1055, 1216, 1382, 1543, 1705, 1865, 2027, 2188, 2350 and 2510, respectively for one set of embodiments. In accordance with another set of embodiments of the amplifier, the amplifier gain is fixed at the lowest above value, i.e., for this example, 88, so as to provide for avoiding the relative degradation of the associated signal-to-noise ratio (SNR) that naturally occurs with the relatively high gain levels of the programmable-gain set of embodiments.

It should be understood that the associated processes of the Data Recording Application (DRA) 14 could be implemented either in software-controlled hardware, hardware, or a combination of the two.

For example, in one set of embodiments, either or both the recording module 13 or docking system 27 may be constructed and operated in accordance with the disclosure of U.S. Provisional Application No. 62/575,364 filed on 20 Oct. 2017, entitled CORONARY ARTERY DISEASE DETECTION SYSTEM, which is incorporated by reference in its entirety. Furthermore, in accordance with one set of embodiments, the auscultatory coronary-artery-disease detection system 10 may further incorporate an ECG sensor 34, for example, in one set of embodiments, an ECG sensor 34′ comprising a pair of electrodes incorporated in a corresponding pair of auscultatory sound sensors 12, wherein the signal from the ECG sensor 34′ is also preprocessed and recorded by a different signal channel of the same hardware-based signal conditioning/preprocessing and recording subsystem 25 of the recording module 13 that is used to preprocess the signals from the one or more auscultatory sound sensors 12. Alternatively, the ECG sensor 34 may comprise a separate set of a pair or plurality of electrodes that are coupled to the skin of the test subject, for example, in one set of embodiments, a pair of signal electrodes 35, 35 ^(+/−) in cooperation with a ground electrode 35 ⁰, wherein, referring to FIG. 3 (illustrating the locations of the electrodes 35, 35 ^(+/−), 35 ⁰), the signal electrodes 35, 35 ^(+/−) span the heart from diametrically-opposed quadrants of the torso 44, and the ground electrode 35 ⁰ is located in a different quadrant, orthogonally displaced from a midpoint of a baseline connecting the signal electrodes 35, 35 ^(+/−). Furthermore, in one set of embodiments, the recording module 13 and docking system 27 may each incorporate a corresponding respective USB interface 36.1, 36.2 to provide for transferring corresponding auscultatory sound signals 16 and or an ECG signal 37 from the recording module 13 to the docking system 27, for example, rather than relying upon the first 18 and second 26 wireless transceivers of an associated wireless interface 26′. Further alternatively, either instead of, or in addition to, the wireless interface 26′ or the USB interface 36.1, 36.2, data may be transferred from the recording module 13 to the docking system 27 via a portable memory element, for example, either an SD memory card or a Micro SD memory card.

The functionality of the Data Recording Application (DRA) 14 is distributed across the recording module 13 and the docking system 27. For example, referring to FIG. 2, in accordance with a first aspect 10′ of an auscultatory coronary-artery-disease detection system 10, 10′, the Data Recording Application (DRA) 14 spans across the recording module 13 and the docking system 27, with a first portion 14.1 comprising the hardware-based signal conditioning/preprocessing and recording subsystem 25 operative on the recording module 13, and a remaining second portion 14.2 operative on the docking system 27. Alternatively, as another example, referring to FIG. 4, in accordance with a second aspect 10″ of an auscultatory coronary-artery-disease detection system 10, 10″, the Data Recording Application (DRA) 14 is operative entirely on the recording module 13.

The auscultatory sound sensor 12 provides for sensing sound signals that emanate from within the thorax 20 of the test-subject 22 responsive to the operation of the test-subject's heart, and the resulting flow of blood through the arteries and veins, wherein an associated build-up of deposits therewithin can cause turbulence in the associated blood flow that can generate associated cardiovascular-condition-specific sounds, the latter of which can be detected by a sufficiently-sensitive auscultatory sound sensor 12 that is acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22. For some cardiovascular-conditions associated with, or predictive of, a cardiovascular disease, the sound level of these cardiovascular-condition-specific sounds can be below a level that would be detectable by a human using a conventional stethoscope. However, these sound levels are susceptible to detection by sufficiently sensitive auscultatory sound sensor 12 that is sufficiently acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22. For example, in one of embodiments, the auscultatory sound sensor 12 may be constructed in accordance with the teachings of U.S. Provisional Application No. 62/568,155 filed on 4 Oct. 2017, entitled AUSCULTATORY SOUND SENSOR. Furthermore, in another set of embodiments, the auscultatory sound sensor 12 may be constructed in accordance with the teachings of U.S. Pat. Nos. 6,050,950, 6,053,872 or 6,179,783, which are incorporated herein by reference.

Referring also to FIG. 5a , the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) are acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22 via an acoustic interface 40, for example, via a hydrogel layer 40′, that also functions as an associated adhesive interface 42 that is attached to the associated auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) with a first adhesive layer 42.1, for example, either a first surface 40.1′ of the hydrogel layer 40′ or a first layer of double-sided tape 42.1′ on a first side of the acoustic/adhesive interface 40, 42, and that is attached to the skin 38 of the thorax 20 of the test-subject 22 with a second adhesive layer 42.2, for example, either a second surface 40.2′ of the hydrogel layer 40′ or a second layer of double-sided tape 42.2′ on the opposing, second side of the acoustic/adhesive interface 40, 42. When fully coupled—as illustrated in FIG. 5a ,—the auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is fully attached to the acoustic/adhesive interface 40, 42 via the first adhesive layer 42.1, 42.1′, 40.1′, and the acoustic/adhesive interface 40, 42 is fully attached to the skin 38 of the thorax 20 of the test-subject 22 via the second adhesive layer 42.2, 42.2′, 40.2′, so that sound signals from within the thorax 20 of the test-subject 22 can propagate otherwise unimpeded to the auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″), thereby providing for a maximum achievable level of the corresponding associated auscultatory sound signals 16, and thereby improving the prospect of detecting an associated abnormal cardiovascular condition—if present—therefrom. Referring to FIGS. 5b and 5c —with the acoustic/adhesive interface 40, 42 respectively detached from the skin 38 or detached from the auscultatory sound sensor 12, respectively,—if the auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is completely detached from the skin 38 of the thorax 20 of the test-subject 22, and thereby fully decoupled therefrom, the resulting auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) would be substantially non-responsive to sound signals from within the thorax 20 of the test-subject 22. Referring to FIGS. 5d to 5g , if the auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is partially attached to the skin 38 of the thorax 20 of the test-subject 22, and thereby partially decoupled therefrom—i.e., in a condition referred to herein as being debonded therefrom—the resulting auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) would be only partially responsive to sound signals from within the thorax 20 of the test-subject 22, but not sufficiently responsive to provide for an associated auscultatory sound signal 16 of sufficient amplitude to provide for reliably detecting a prospective associated abnormal cardiovascular condition. More particularly, FIGS. 5d and 5e respectively illustrate an acoustic/adhesive interface 40, 42 partially detached from skin 38, and an acoustic/adhesive interface 40, 42 partially detached from an auscultatory sound sensor 12, respectively. Furthermore, FIG. 5f illustrates an auscultatory sound sensor 12 attached to a wrinkled acoustic/adhesive interface 40, 42, and FIG. 5g illustrates an acoustic/adhesive interface 40, 42 attached to wrinkled skin 38. In anticipation of prospective problems with nature of the attachment of the acoustic/adhesive interface 40, 42, the Data Recording Application (DRA) 14 is provided with a means—described more fully hereinbelow—for detecting if one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is, or are, either detached or debonded from the skin 38 of the thorax 20 of the test-subject 22, so as to provide for excluding data from auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) that are either detached or debonded, from the skin 38 of the thorax 20 of the test-subject 22 from being used to diagnose a prospective abnormal cardiovascular condition.

Generally, the adhesive interface 42 could comprise either a hydrogel layer 40′, for example, P-DERM® Hydrogel; a silicone material, for example, a P-DERM® Silicone Gel Adhesive; an acrylic material, for example, a P-DERM® Acrylic Adhesive; a rubber material; a synthetic rubber material; a hydrocolloid material; or a double-sided tape, for example, with either rubber, acrylic or silicone adhesives.

Referring to FIG. 6, it has been found that the quality of the auscultatory sounds acquired from a test-subject 34 can be improved if the torso 44 of the test-subject 34 is inclined, for example, in one set of embodiments, at an inclination angle θ of about 30 degrees above horizontal—but generally, as close to upright (i.e. θ=90 degrees) as can be accommodated by the associated adhesive interface(s) 42 of the associated auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″)—which imposes a transverse component of gravitational force on each of the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) that is resisted by the associated adhesive interface(s) 42.

Referring to FIGS. 7-15, in accordance with a first aspect 700, an auscultatory-sound-sensing process 700 provides for first determining a scale factor SF from an initially-acquired block of auscultatory-sound-sensor time-series data S, and initially determining from the scale factor SF whether one or more of the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is detached from the skin 38 of the thorax 20 of the test-subject 22, wherein when multiplied by the scale factor SF, the values of the associated auscultatory-sound-sensor time-series data S are nominally within a range that is a predetermined percentage of the dynamic range of the associated data acquisition system (for example, that provides 16-bit signed digital values). If there is no detachment, the first aspect 700, the auscultatory-sound-sensing process 700 provides for acquiring successive blocks of auscultatory-sound-sensor time-series data S while the test-subject 22 is holding their breath, and determining from each block of auscultatory-sound-sensor time-series data S—using an associated predetermined debond-detection threshold—whether or not one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is debonded from the skin 38 of the thorax 20 of the test-subject 22, or whether there is excessive noise in the auscultatory-sound-sensor time-series data S. The auscultatory-sensor time-series data S is rejected if excessive noise is detected, and the test is aborted if one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) has become decoupled from the skin 38 of the thorax 20.

More particularly, referring to FIG. 7, the first aspect 700 of the auscultatory-sound-sensing process 700 commences with step (702) by initializing a data-block counter i to a value of zero, and then, in step (704), acquiring a block of N_(S) contiguous samples of auscultatory-sound-sensor time-series data S in accordance with a first aspect 800 of a data acquisition process 800. This initially-acquired data is then used to determine a scale factor SF that is used to determine whether or not one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is/are detached from the skin 38 of the thorax 20 of the test-subject 22, and then subsequently to scale subsequent blocks of time-series data S. The initial block of auscultatory-sound-sensor time-series data S may be acquired either with, or without, the test-subject 22 holding their breath, but typically with the test-subject 22 allowed to breath normally—for their comfort and convenience. The number of samples N_(S) to be acquired is given by the product of an acquisition period δ_(i) in seconds, times a sampling frequency Fs in Hz. For example, in one set of embodiments, in step (704), the initially-acquired block of auscultatory-sound-sensor time-series data S typically contains 10 seconds of data, which at a sampling frequency Fs of 24 KHz, results in N_(S)=δ_(i)*Fs=240,000 samples.

More particularly, referring to FIG. 8, the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) with an analog anti-aliasing filter, for example, an analog second-order band-pass filter having a pass band in the range of 20 Hz to 1 KHz, for which the upper-cutoff frequency is sufficiently below the sampling frequency (i.e. no more than half) so as to prevent high frequency components in the signal being sampled from appearing as low frequency components of the sampled signal. Following step (802), if, in step (804), the test-subject 22 need not necessarily hold their breath—as is the case for the initially-acquired block of auscultatory-sound-sensor time-series data S,—then, in step (806), the pre-filtered auscultatory sound signal 16 is sampled at the sampling frequency Fs and converted to digital form by the associated analog-to-digital (ADC) converter. Then, from step (808), the auscultatory sound signal 16 continues to be sampled in step (806) until N_(S) samples of the block of auscultatory-sound-sensor time-series data S have been acquired, after which, in step (810), the N_(S) samples of auscultatory-sound-sensor time-series data S are returned to step (704) of the auscultatory-sound-sensing process 700. For example, FIGS. 9 and 10 a each illustrate a first block of auscultatory-sound-sensor time-series data S of duration δ₀ that was recorded from one of the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″).

Referring again to FIG. 7, following step (704), in step (706), the scale factor SF is determined from the initially-acquired block of auscultatory-sound-sensor time-series data S, in accordance with an associated scale-factor-determination process 1100. More particularly, referring to FIGS. 11 and 12 a, the scale-factor-determination process 1100 commences with step (1102), wherein the block of auscultatory-sound-sensor time-series data S is divided into a plurality of data segments 46, for example, each of the same data-segment duration δ_(D) that nominally spans a single heartbeat, for example, about one second. For example, in FIG. 12a , the δ₀=10 second block of auscultatory-sound-sensor time-series data S is divided into N_(Seg)=10 data segments 46, each of δ_(D)=one second duration. FIG. 12a illustrates a time series |S| containing the absolute values of the auscultatory-sound-sensor time-series data S illustrated in FIG. 10a . As indicated by X's in FIG. 12a , for each data segment 46, m spanning the range k=k_(MIN)(m) to k=k_(MAX)(m) of the auscultatory-sound-sensor time-series data S(k), the corresponding maximum absolute value of the auscultatory-sound-sensor time-series data S(k) is determined, as given by:

S _(m) ^(MAX)=Max(|S(k)|)_(k) _(MIN) _((m)≤k≤k) _(MAX) _((m))  (1)

Then, in step (1104), the median of these maximum values is determined, as given by

median(S _(1≤m≤N) _(Seg) ^(MAX))  (2)

Finally, in step (1106), the scale factor SF is determined, as given by:

$\begin{matrix} {{SF} = \frac{{DR}_{0 - P}}{{median}\left( S_{1 \leq m \leq N_{Seg}}^{MAX} \right)}} & (3) \end{matrix}$

wherein DR_(0-p) is the nominal zero-to-peak dynamic range of the auscultatory-sound-sensor time-series data S after scaling, i.e. after multiplying the acquired values by the scale factor SF. For example, in one set of embodiments, the nominal zero-to-peak dynamic range is set to be about 80 percent—more broadly, but not limiting, 80 percent plus or minus 15 percent—of the zero-to-peak range of the associated analog-to-digital converter—for example, in one set of embodiments, a 16-bit signed analog-to-digital converter—used to digitize the auscultatory-sound-sensor time-series data S in step (806). In one set of embodiments, the scale factor SF is integer-valued that, for an attached and bonded auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″), ranges in value between 1 and 28.

If one or more of the associated auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is detached from the skin 38 of the thorax 20 of the test-subject 22, then the associated level of the auscultatory sound signals 16 will be low—for example, at a noise level—resulting in a relatively large associated scale factor SF from step (1106). Accordingly, if, in step (1108), the scale factor SF is in excess of an associated threshold SF_(MAX), then the Data Recording Application (DRA) 14 is aborted in step (1110), and the operator 48 is alerted that the one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is/are detached, so that this can be remedied. For example, in one set of embodiments, the value of the threshold SF_(MAX) is 28 for the above-described fixed-gain embodiment, i.e. for which the associated amplifier has a fixed gain of 88, feeding a 16-bit analog-to-digital converter (ADC) that provides for converting a +/−5 volt input signal to +/−32,767. Otherwise, from step (1108), if the value of the scale factor SF does not exceed the associated threshold SF_(MAX), in step (1112), the scale factor SF is returned to step (706) for use in scaling subsequently-recorded breath-held auscultatory sound signals 16.1.

Referring again to FIG. 7, following step (706), in step (708), the value of the data-block counter i is incremented, so as to point to the next block of auscultatory-sound-sensor time-series data S to be recorded. If, while this next block is recorded, the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) remain attached and bonded to the skin 38 of the thorax 20 of the test-subject 22, and the associated breath-held auscultatory sound signals 16.1 are not corrupted by excessive noise, then this next block of auscultatory-sound-sensor time-series data S will then be subsequently used to detect a prospective abnormal cardiovascular condition therefrom. The auscultatory sound signals 16 used to detect prospective abnormal cardiovascular conditions are recorded while the test-subject 22 holds their breath, the latter to prevent the resulting cardiovascular-based auscultatory sound signals 16 from being overwhelmed by breathing-related sounds that are substantially louder than cardiovascular-based sounds, thereby providing for improving the associated signal-to-noise ratio of the cardiovascular-based auscultatory sound signals 16.

Accordingly, in step (710), a next block of N_(S) contiguous samples of auscultatory-sound-sensor time-series data S is acquired over an acquisition period δ_(i) in accordance with a first aspect 800 of a data acquisition process 800, during which time the test-subject 22 is instructed to hold their breath. For example, in one set of embodiments, the nominal acquisition period δ_(i) is 10 seconds—but at least 5 seconds,—which, at a sampling frequency Fs of 24 KHz, results in N_(S)=δ_(i)*Fs=240,000 samples.

More particularly, referring again to FIG. 8, the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ³, 12 ^(1″), 12 ^(2″), 12 ^(3″) with the above-described analog anti-aliasing filter. Then, from step (804), because breath-held data is to be acquired, in step (812), the test-subject 22 is instructed by the operator 48 to hold their breath. In step (814), if the operator 48 observes that the test-subject 22 is compliant in holding their breath, or, additionally or alternatively, if this is confirmed by a below-described breath-hold detection process 1400, then upon manual initiation by the operator 48, in step (816), a sample counter j is initialized to a value of one, and, in step (818), the next sample of pre-filtered auscultatory sound signal 16 is sampled at the sampling frequency Fs and converted to digital form by an associated analog-to-digital (ADC) converter. This process continues until either N_(S)=δ_(i)*Fs samples have been acquired, or the test-subject 22 resumes breathing. More particularly, in step (820), if one or more addition samples remain to be acquired, and if the operator 48 continues to observe that the test-subject 22 is holding their breath, or, additionally or alternatively, if this is confirmed by a below-described breath-hold detection process 1400, then, in step (822) the sample counter j is incremented, and the next sample is acquired in step (818). Otherwise, from step (820), if either all N_(S)=δ_(i)*Fs samples have been acquired, or if either the operator 48 observes that the test-subject 22 has resumed breathing, or, additionally or alternatively, if this is confirmed by a below-described breath-hold detection process 1400, or, if the test-subject 22 indicates by their own separate manual switch input that they will resume breathing, then, in step (824), the data acquisition process 800 terminates, and the block of breath-held auscultatory-sound-sensor time-series data S containing N_(S)=j samples is returned to step (710). In one set of embodiments, if, following step (814), the test-subject 22 is not holding their breath, the pre-filtered auscultatory sound signals 16 are also separately-recorded while waiting for the test-subject 22 to hold their breath, or resume doing so. In practice, the auscultatory sound signals 16 typically continue to be recorded between breath-held segments, that latter of which are identified by associated recorded start and stop times with respect to the associated continuous recording.

Referring also to FIG. 13, alternatively, or additionally, in step (814), the auscultatory coronary-artery-disease detection system 10 may further incorporate an accelerometer 50 operatively coupled to the thorax 20 of the test-subject 22 to provide an associated acceleration signal responsive to the motion of the thorax 20. With the test-subject 22 lying on their back at an inclined angle, for example, at about 30 degrees above horizontal, for example, as illustrated in FIG. 4 of U.S. Provisional Application No. 62/568,155 filed on 4 Oct. 2017, entitled AUSCULTATORY SOUND SENSOR, which has been incorporated by reference herein in its entirety, the associated acceleration signal—operatively coupled to recording module 13 and possibly transmitted to the docking system 27—may be twice integrated either in recording module 13 or the docking system 27 to generate a measure of the peak-to-peak displacement of the thorax 20, which if greater than a threshold would be indicative of breathing by the test-subject 22.

More particularly, referring also to FIG. 14, for an auscultatory coronary-artery-disease detection system 10 incorporating an accelerometer 50 operatively coupled to the thorax 20 of the test-subject 22, an acceleration signal 52 therefrom may alternatively or additionally be processed by an associated breath-hold detection process 1400 to provide for automatically determining—for example, in step (814) of the data acquisition process 800 illustrated in FIG. 8—whether or not the test-subject 22 is breathing, responsive to the determination, from the acceleration signal 52, of a peak-to-peak displacement of the thorax 20 of the test-subject 22. More particularly, beginning with, and in, step (1402), the respective previous/initial values of thorax displacement Y₀ and thorax velocity V₀, respectively, are each initialized to values of zero; a sample counter i is initialized to an initial value, for example, zero; the respective minimum Y_(MIN) and maximum Y_(MAX) values of thorax displacement are each set equal to the (initial) value of thorax displacement Y₀; the values of the sample counter i_(MIN) and i_(MAX) at which the corresponding minimum Y_(MIN) and maximum Y_(MAX) values of thorax displacement occur are set equal to the initial value of the sample counter i; and a BreathingFlag is set to indicate that the test-subject 22 is breathing. Then, in step (1404), the current sample of thorax acceleration A is acquired. Then, in step (1406), if the sample counter i is equal to the initial value, i.e. i==0, then, in step (1408), the previous value of thorax acceleration A₀ (i.e. initially, the initial value thereof) is set equal to the value of the current sample of thorax acceleration A, i.e. A₀==A, and then, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).

Otherwise, from step (1406), if the current sample of thorax acceleration A is not the first sample, then, in step (1412), the current value of thorax velocity V is calculated by integrating the previous A₀ and current A measurements of thorax acceleration, for example, using a trapezoidal rule, as follows:

$\begin{matrix} {V = {{\frac{\left( {A_{0} + A} \right)}{2} \cdot {dt}} + V_{0}}} & (4) \end{matrix}$

wherein dt is the time period between samples, i.e. dt=1/Fs. Then, in step (1414), the current value of thorax displacement Y is calculated by integrating the above-calculated previous V₀ and current V values of thorax velocity, for example, again using a trapezoidal rule, as follows:

$\begin{matrix} {Y = {{\frac{\left( {V_{0} + V} \right)}{2} \cdot {dt}} + Y_{0}}} & (5) \end{matrix}$

Then, in step (1416), the respective previous values of thorax acceleration A₀, thorax displacement Y₀ and thorax velocity V₀ are respectively set equal to the corresponding current values of thorax acceleration A, thorax velocity V and thorax displacement Y, respectively, that will each be used in subsequent iterations of steps (1412) and (1414).

Then, in step (1418), if the current value of thorax displacement Y is greater than then current maximum value of thorax displacement Y_(MAX)—for example, as would result during a phase of chest expansion by the test-subject 22,—then, in step (1420), the current maximum value of thorax displacement Y_(MAX) is set equal to the current value of thorax displacement Y and the corresponding value of the sample counter i_(MAX) associated therewith is set to the current value of the sample counter i. Otherwise, from step (1418)—for example, as would result from other than a phase of chest expansion by the test-subject 22,—if, in step (1422), the amount by which the current value of the sample counter i exceeds the value of the sample counter i_(MAX) associated with the maximum value of thorax displacement Y_(MAX) is not equal to a threshold value Δ (the relevance of which is described more fully hereinbelow), then in step (1424), if the current value of thorax displacement Y is less than then current minimum value of thorax displacement Y_(MIN)—for example, as would result during a phase of chest contraction by the test-subject 22,—then, in step (1426), the current minimum value of thorax displacement Y_(MIN) is set equal to the current value of thorax displacement Y and the corresponding value of the sample counter i_(MIN) associated therewith is set to the current value of the sample counter i. From either steps (1420) or (1426), in step (1428), if the amount by which the current maximum value of thorax displacement Y_(MAX) is greater the current minimum value of thorax displacement Y_(MIN) meets or exceeds a displacement threshold ΔY_(MAX), then, in step (1430), the BreathingFlag is set to indicate that the test-subject 22 is breathing, after which, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404). Similarly, from step (1428), if the displacement threshold ΔY_(MAX) is not exceeded, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404). Further similarly, from step (1424)—for example, as would result from other than a phase of chest contraction by the test-subject 22,—if, in step (1432), the amount by which the current value of the sample counter i exceeds the value of the sample counter i_(MIN) associated with the minimum value of thorax displacement Y_(MIN) is not equal to the threshold value Δ, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).

If, from step (1432), the amount by which the current value of the sample counter i exceeds the value of the sample counter i_(MIN) associated with the minimum value of thorax displacement Y_(MIN) is equal to the threshold value Δ—following a minimum chest contraction of the test-subject 22, in anticipation of subsequent chest expansion, wherein the threshold value Δ is greater than or equal to one,—then, in step (1434), the peak-to-peak thorax displacement ΔY is calculated as the difference between the current maximum Y_(MAX) and minimum Y_(MIN) values of thorax displacement, and, in step (1436), the maximum value of thorax displacement Y_(MAX) is set equal to the current value of thorax displacement Y, and the value of the sample counter i_(MAX) at which the corresponding maximum value of thorax displacement Y_(MAX) occurred is set equal to the current value of the sample counter i, in anticipation of subsequently increasing magnitudes of the current value of thorax displacement Y to be tracked in steps (1418) and (1420).

Similarly, if, from step (1422), the amount by which the current value of the sample counter i exceeds the value of the sample counter i_(MAX) associated with the maximum value of thorax displacement Y_(MAX) is equal to the threshold value Δ—following a maximum chest expansion of the test-subject 22, in anticipation of subsequent chest contraction, wherein the threshold value Δ is greater than or equal to one,—then, in step (1438), the peak-to-peak thorax displacement ΔY is calculated as the difference between the current maximum Y_(MAX) and minimum Y_(MIN) values of thorax displacement, and, in step (1440), the minimum value of thorax displacement Y_(MIN) is set equal to the current value of thorax displacement Y, and the value of the sample counter i_(MIN) at which the corresponding minimum value of thorax displacement Y_(MIN) occurred is set equal to the current value of the sample counter i, in anticipation of subsequently decreasing magnitudes of the current value of thorax displacement Y to be tracked in steps (1424) and (1426).

From either steps (1436) or (1440), in step (1442), if the amount of the peak-to-peak thorax displacement ΔY calculated in steps (1434) or (1438), respectively, meets or exceeds the displacement threshold ΔY_(MAX), then, in step (1444), the BreathingFlag is set to indicate that the test-subject 22 is breathing. Otherwise, from step (1442), if the amount of the peak-to-peak thorax displacement ΔY calculated in steps (1434) or (1438), respectively, does not exceed the displacement threshold ΔY_(MAX), then, in step (1446), the BreathingFlag is reset to indicate that the test-subject 22 is not breathing. Following either step (1444) or (1446), in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).

Referring again to FIG. 7, in step (712), a corresponding block of scaled auscultatory-sound-sensor time-series data Ŝ is calculated by multiplying the acquired block of auscultatory-sound-sensor time-series data S by the scale factor SF, and in step (714), the scaled auscultatory-sound-sensor time-series data Ŝ is analyzed by an associated debond detection process 1500 to determine whether or not any of the auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) was debonded from skin 38 of the thorax 20 of the test-subject 22 during the associated data acquisition process 800.

More particularly, referring to FIG. 15, the debond detection process 1500 commences with step (1502) by initializing a sample counter j to a value of one, and initializing a threshold counter TC to a value of zero, wherein the threshold counter TC is a count of the number of contiguous successive samples for which the value of the scaled auscultatory-sound-sensor time-series data Ŝ is less than an associated predetermined debond-detection threshold DT. For example, in one set of embodiments, the debond-detection threshold DT is set to a value that is about 20% of the achievable maximum value of the output from the analog-to-digital converter (ADC). Then, in step (1504), if the absolute value of the sample of scaled auscultatory-sound-sensor time-series data Ŝ, i.e. |S (j)|, is less than the predetermined debond-detection threshold DT (or, alternatively, if |SF*S(j)|<DT, thereby precluding a need to separately calculate and store scaled auscultatory-sound-sensor time-series data Ŝ), then in step (1506), the threshold counter TC is incremented, after which, in step (1508), if the value of the threshold counter TC does not exceed the number of samples in N_(D) successive data segments 46, i.e. TC<N_(D)*δ_(D)*Fs, and in step (1510), if the sample counter j does not exceed the number of samples N_(S) in the block of scaled auscultatory-sound-sensor time-series data Ŝ, then, in step (1512), the sample counter j is incremented, and the process continues again with step (1504). Otherwise, from step (1504), if the absolute value of the current sample of scaled auscultatory-sound-sensor time-series data Ŝ, i.e. |Ŝ(j)|, is not less than the predetermined debond-detection threshold DT—indicating that the auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is not debonded from the skin 38 of the thorax 20 of the test-subject 22,—then, in step (1514), the threshold counter TC is reset to a value of zero and the process continues with step (1510). Otherwise, from step (1510), if the sample counter j exceeds the number of samples N_(S) in the block of scaled auscultatory-sound-sensor time-series data Ŝ or auscultatory-sound-sensor time-series data S—indicating that the entire block of scaled auscultatory-sound-sensor time-series data Ŝ or auscultatory-sound-sensor time-series data S has been screened,—then the debond detection process 1500 is terminated with step (1516) by returning an indication to step (714) of the auscultatory-sound-sensing process 700 that the associated auscultatory sound sensor 12, 12 ^(v), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is not debonded from the skin 38 of the thorax 20 of the test-subject 22. Otherwise, from step (1508), if the value of the threshold counter TC exceeds the number of samples in N_(D) successive data segments 46, then the debond detection process 1500 is terminated with step (1518) by returning an indication to step (714) of the auscultatory-sound-sensing process 700 that the associated auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) is debonded from the skin 38 of the thorax 20 of the test-subject 22. For example, in one set of embodiments, the value of N_(D) is equal to 4, and the value of δ_(D) is equal to 1 second.

Returning to FIG. 7, in step (716), if, from step (714), the debond detection process 1500 detected that the associated auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) was debonded while acquiring the block of auscultatory-sound-sensor time-series data S, then the Data Recording Application (DRA) 14 is aborted in step (718), and the operator 48 is alerted that one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) are debonded, so that this can be remedied. Otherwise, if, from step (714), the debond detection process 1500 did not detect that the associated auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) was debonded while acquiring the block of auscultatory-sound-sensor time-series data S, then, in step (720), if sufficient noise-screened data has not been gathered—for example, in one set of embodiments, a total duration of at least 65 seconds of recorded data,—then the auscultatory-sound-sensing process 700 continues with step (708).

In step (724), an associated noise detection (i.e. noise-screening) process—operating on either the block of scaled auscultatory-sound-sensor time-series data Ŝ, or the block of auscultatory-sensor time-series data S, in parallel with the debond detection process 1500—provides for detecting if the block of auscultatory-sound-sensor time-series data S has been corrupted by excessive noise, and if so, from step (726), that block of auscultatory-sound-sensor time-series data S is ignored, and the auscultatory-sound-sensing process 700 continues by repeating step (710) to acquire a new block of auscultatory-sound-sensor time-series data S. Otherwise, from step (726), if the block auscultatory-sound-sensor time-series data S has not been corrupted by excessive noise, the process continues with the above-described step (720).

From step (720), if sufficient noise-screened data has been gathered for which the associated one or more auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) were not debonded from the skin 38 of the thorax 20 of the test-subject 22—for example, in one set of embodiments, a total duration of at least 65 seconds of recorded data,—then, in step (722), at least the composite set of blocks of breath-held auscultatory-sound-sensor time-series data S acquired in step (710) are subsequently analyzed by an associated Data Analysis Application (DAA) 54 operative on the docking system 27—as illustrated in FIGS. 2 and 3—so as to provide for detecting an abnormal cardiovascular condition of the test-subject 22. In addition to recording the segments of breath-held data, alternatively, all data may be recorded and provided to the Data Analysis Application (DAA) 54, along with an associated index that provides for identifying the corresponding associated breath-held portions thereof for which the associated auscultatory sound sensors 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) were neither detached nor debonded from the skin 38 of the thorax 20 of the test-subject 22, nor corrupted by noise.

FIGS. 9 and 10 a-10 f illustrate a simulation of six blocks of breath-held auscultatory-sound-sensor time-series data S recorded in accordance with the first aspect 700 of auscultatory-sound-sensing process 700, with respective durations of δ₁, δ₂, δ₃, δ₄, δ₅, and δ₆ during which time periods the test-subject 22 was holding their breath, separated by periods Δ₁, Δ₂, Δ₃, Δ₄, and Δ₅ of normal breathing, wherein FIGS. 10a-10e illustrate breath-held auscultatory sound signals 16.1, 16.1′ from a normally-bonded auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″) as illustrated in FIG. 5a , and FIG. 10f illustrates a breath-held auscultatory sound signal 16.1, 16.1″ from a debonded auscultatory sound sensor 12, 12 ^(1′), 12 ^(2′), 12 ^(3′), 12 ^(1″), 12 ^(2″), 12 ^(3″), for example, as illustrated in any of FIGS. 5d-5g , for example, as might be caused by excessive hair between the adhesive interface 40 and the auscultatory sound sensor 12, poor placement of the auscultatory sound sensor 12 on the thorax 20 of the test-subject 22, poor angular orientation of the auscultatory sound sensor 12 relative to the surface of the skin 38, or wrinkled adhesive interface 40 between the auscultatory sound sensor 12 and the skin 38, For purposes of simplicity of illustration, FIGS. 10b-10e are identical to FIG. 10a . However, it should be understood that typically the amplitude of the auscultatory sound signals 16, 16.1 varies from heartbeat to heartbeat, and from one breath-held segment to another.

Alternatively, one or more of the auscultatory-sound-sensing process 700, the data acquisition process 800, the scale-factor-determination process 1300, or the de-bond detection process 1500 could be implemented with corresponding alternative processes disclosed in U.S. application Ser. No. 16/136,015 filed on 19 Sep. 2018—with particular reference to FIGS. 16 through 22—which is incorporated by reference herein in its entirety.

Referring to FIG. 16, in one set of embodiments, all the data is recorded throughout the duration of the test, including segments both with and without breathing, and a set of index pointers are used to identify locations of associated events, for example, index pointer arrays i₀[ ] and i₁[ ] to store the sample locations at the beginning and end of breath-held data segments of the corresponding sampled auscultatory sound data S^(m)[ ] from the m^(th) auscultatory sound sensor 12, and later-used index pointer arrays i_(S1)[ ] and i_(S2)[ ] to store the sample locations of the S1 sound at the beginning of each heart cycle, and the S2 sound at the beginning of diastole respectively, wherein in FIG. 16, the symbol “B” is used to indicate a period of breathing, the symbol “H” is used to indicate a period of breath-holding, and the symbol “D” is used to indicate a period of diastole. A status array Status[m, k] indicates the measurement status of the k^(th) breath-held data segment of the m^(th) auscultatory sound signal 16, i.e. the sampled auscultatory sound data S^(m)( ) from the m^(th) auscultatory sound sensor 12. Accordingly, step (728) that provides for ignoring data may be implemented by setting the corresponding value of the status array Status(m, k) to a value of IGNORE.

Referring to FIGS. 17-20, a noise detection process 1700 called from step (724) provides for determining whether or not a particular segment of breath-held auscultatory sound signal 16.1 is corrupted by excessive noise, and if so, provides for flagging that segment as being excessively noisy so as to be prospectively excluded from subsequent processing. Generally the noise detection process 1700 generates, in step (1714), frequency-domain noise filters FH[ ] responsive to cross-correlations of frequency spectra of pairs of adjacent auscultatory sound sensors 12. Accordingly, sensor-index pointers m1[p] and m2[p] provide for identifying the associated auscultatory sound sensors 12, m1[p], m2[p] of each pair, the latter of which is identified by pair pointer p. For each pair of adjacent auscultatory sound sensors 12 in a set of adjacent pairs—selected by sensor-index pointers m1[p] and m2[p] in steps (1706) and (1710), respectively, wherein p is a pair-pointer initialized to 1 in step (1704)—noise detection process 1700 generates, in step (1714), a frequency-domain noise filter FH[ ] by cross-correlating in step (1802) the frequency spectra FS^(A)[ ] and FS^(B)[ ] (generated in steps (1708) and (1712) of each associated breath-held auscultatory sound signal 16.1: S^(A)[ ] and S^(B)[ ], wherein the values of the frequency-domain noise filter FH[ ] are generated by normalizing the frequency spectra of the cross-correlation function to a range of 0 to 1 in step (1804), then subtracting these values from unity, and then setting resulting values that are less than a noise floor to the value of the noise floor in step (1806). Accordingly, when used to multiply the frequency spectra FS^(A)[ ] and FS^(B)[ ] in step (1814) as called from steps (1718) and (1720) for frequency spectra FS^(A)[ ] and FS^(B)[ ], respectively, the frequency-domain noise filter FH[ ] provides for attenuating the components of the breath-held auscultatory sound signal 16.1: S^(A)[ ] and S^(B)[ ] that are correlated with one another. Accordingly, the operation of step (1904) provides for a matched filtering process to accentuate the underlying noise in the associated breath-held auscultatory sound signal 16.1: S^(A)[ ] or S^(B)[ ]. The product of the frequency-domain noise filter FH[ ] with either of the frequency spectra FS^(A)[ ] or FS^(B)[ ] in step (1904) in inverse Fourier transformed back to the corresponding time domain noise signal SN[ ]. Then, in steps (1908) through (1918), a plurality of N_(FFT)-point short-time Fourier transform (STFT) arrays are generated, for example, for N_(FFT)=1024, with each overlapped with respect on one another by a half width, i.e. N_(FFT)/2, after which the associated average spectral power array FX[ ] is calculated in dB in step (1920). Then, the average powers P_(LOW), P_(MID), P_(HIGH) in three respective frequency ranges 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz, respectively, is calculated in steps (1922), (1924) and (1926), respectively, wherein each average power P_(LOW), P_(MID), P_(HIGH) is compared with a corresponding threshold—for example, in one set of embodiments, −20 dB, −50 dB and −30 dB, respectively—in step (1928). The corresponding breath-held auscultatory sound signal 16.1: S^(A)[ ] or S^(B)[ ] is then flagged in steps (1930) as being noisy if any of the associated noise power thresholds are exceeded. Referring to FIG. 20, if one auscultatory sound sensor 12 exceeds the noise threshold in a given kth breath-held segment of breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]], that auscultatory sound sensor 12, m is ignored for that kth breath-held segment. If a particular auscultatory sound sensor 12, m exceeds the noise threshold for more than one breath-held segment of breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]], then that auscultatory sound sensor 12, m is flagged as being bad. For each breath-held segment k of data, the process of steps (1706) through (1722) repeats for each of N_(PAIRS) pairs of adjacent auscultatory sound sensors. For example, referring to FIG. 3, in one set of embodiments, there are three pairs of adjacent auscultatory sound sensors, i.e. N_(PAIRS)=3, as follows: for p=1, the auscultatory sound sensors 12 at the left and sternum second intercostal spaces L2, S2; for p=2, the auscultatory sound sensors 12 at the left and sternum third intercostal spaces L3, S3; and for p=3, the auscultatory sound sensors at the left and sternum fourth intercostal spaces L4, S4. The process of steps (1704) through (1726) is repeated until all the breath-held segments k of data have been processed.

More particularly, referring to FIG. 17, the noise detection process 1700 commences with step (1702) by initializing a breath-held-segment pointer k to a value of 1, so as to provide for pointing to the first breath-held segment of data. Generally, the breath-held-segment pointer k provides for pointing to the kth breath-held segment of data, of duration δ_(k), extending between sample locations i₀ [k] and i₁ [k], as illustrated in FIG. 16. Then, in step (1704), the pair pointer p is initialized to a value of 1, and a noise counter N_(NOISY) is initialed to a value of 0. Then, in step (1706), the kth breath-held segment of breath-held sampled auscultatory sound data S^(m1[p])[i₀ [k]: i₁ [k]] is selected as the sampled auscultatory sound data S^(A)[ ] of the first auscultatory sound sensors 12, m1[p] of the pair p, and in step (1708) the Fourier Transform of the sampled auscultatory sound data S^(A)[ ] is calculated as FS^(A)[ ]=FFT(S^(A)[ ]). Similar, then, in step (1710), the kth breath-held segment of breath-held sampled auscultatory sound data m^(m2[p])[i₀ [k]: i₁ [k]] is selected as the sampled auscultatory sound data S^(B)[ ] of the second auscultatory sound sensor 12, m2[p] of the pair p, and in step (1712) the Fourier Transform of the sampled auscultatory sound data S^(A)[ ] is calculated as FS^(B)[ ]=FFT(S^(A)[ ]).

Then, in step (1714), the frequency-domain noise filter FH[ ] generated by a matched-noise-filter generation process 1800 that—referring also to FIG. 18—commences in step (1802) with the cross correlation of the frequency spectra FS^(A)[ ], FS^(B)[ ] of the sampled auscultatory sound data S^(A)[ ], S^(B)[ ] of the first m1[p] and second m2[p] auscultatory sound sensors 12 of the pair p, wherein the resulting cross-correlation is stored in array FH[ ] and then normalized to a range of 0 to 1 in step (1804), and then inverted in step (1806), wherein each element of the normalized array FH[ ] is subtracted from 1, and if the result is less than a noise floor, is set to the value of the noise floor NoiseFloor. Then, in step (1808), the resulting frequency-domain noise filter FH[ ] is returned and subsequently used in steps (1716) and (1718) of the noise detection process 1700 to evaluate the noise in the frequency spectra FS^(A)[ ], FS^(B)[ ] of the sampled auscultatory sound data S^(A)[ ], S^(B)[ ] of the first m1[p] and second m2[p] auscultatory sound sensors 12 of the pair p, respectively, in accordance with an associated noise-content-evaluation process 1900, the latter of which is called from step (1716) to evaluate the noise content of the frequency spectrum FS^(A)[ ] of the sampled auscultatory sound data S^(A)[ ] of the first auscultatory sound sensor 12, m1[p] of the pair p, and which is called from step (1718) to evaluate the noise content of the frequency spectrum FS^(A)[ ] of the sampled auscultatory sound data S^(A)[ ] of the second auscultatory sound sensor 12, m2[p] of the pair p.

Referring to FIG. 19, the noise-content-evaluation process 1900 commences with step (1902) with receipt of the index m of the associated auscultatory sound sensor 12, m, the associated frequency spectrum FS[ ] of the associated auscultatory sound sensor 12, m, and the associated frequency-domain noise filter FH[ ]. Then, in step (1904), the time domain noise signal SN[ ]—containing a total of NPTS data points, i.e. the number of data points in the breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]]—is given by the inverse Fourier Transform of the product of the associated frequency spectrum FS[ ] with the associated frequency-domain noise filter FH[ ], corresponding to a time-domain cross-correlation of the corresponding associated time-domain signals. Then, in step (1906), each of the N_(FFT) summation data points of a frequency-domain summation array FXSUM[ ] is initialized to zero, as is an associate summation counter N_(SUM), wherein the number of summation data points N_(FFT) is a power of 2, for example, 1024, and substantially less than the total number of data points NPTS in the time domain noise signal SN[ ]. Then, in step (1908), an index j_(MIN)—to the first sample of an N_(FFT)-point window of samples to be analyzed from the time domain noise signal SN[ ]—is initialized to a value of 1. Then, in step (1910), an index j_(MAX)—to the last sample of the N_(FFT)-point window of samples to be analyzed from the time domain noise signal SN[ ]—is set to the value of j_(MIN)+N_(FFT)−1. Then, in step (1912), if the end of the time domain noise signal SN[ ] has not been reached, then, in step (1914), the square of values—i.e. corresponding to noise power—of an N_(FFT) Fourier Transform of the data form the N_(FFT)-point window of samples from the time domain noise signal SN[ ] over the range of samples SN[j_(MIN)] to SN[j_(MAX)], is added to the frequency-domain summation array FXSUM[ ]. Then, in step (1916), the summation counter N_(SUM) is incremented, and in step (1918), the index j_(MIN) is incremented by half the width of the N_(FFT)-point window, i.e. by a value of N_(FFT)/2, so as the provide for the next N_(FFT)-point window to be analyzed to overlap the current N_(FFT)-point window by half the window width. The above noise-content-evaluation process 1900 repeats beginning with step (1910), until, in step (1912), the index j_(MAX) exceeds the end of the time domain noise signal SN[ ], after which, in step (1920), each of the values of the frequency-domain summation array FXSUM[ ] is divided by the value of the summation counter N_(SUM) so as to calculate an associated average noise power FX[ ]. Then, in steps (1922), (1924) and (1926), respectively, the noise power is summed in three different corresponding respective frequency ranges, for example, 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz, respectively, to give three corresponding respective power levels, P_(LOW), P_(MID) and P_(HIGH), respectively. Then, in step (1928), if any of the power levels, P_(LOW), P_(MID) or P_(HIGH) exceeds a corresponding respective power threshold value Threshold_(LOW), Threshold_(MID) or Threshold_(HIGH), then, in step (1930), the noise threshold is considered to have been exceed for the particular auscultatory sound sensor 12, m and the particular associated segment k of breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]] with indication of an associated status in a NoiseStatus flag. Otherwise, from step (1928), if none of the power levels, P_(LOW), P_(MID) or P_(HIGH) exceeds the corresponding respective power threshold value Threshold_(LOW), Threshold_(MID) or Threshold_(HIGH), then, in step (1932), the noise threshold is considered to have not been exceed for the particular auscultatory sound sensor 12, m and the particular associated segment of breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]], with indication of an associated status in the NoiseStatus flag. The results from either steps (1930) or (1932) are then logged by an associated results-logging process 2000 which is called from step (1934).

Referring to FIG. 20, the results-logging process 2000 commences with receipt in step (2002) of the index m of the associated auscultatory sound sensor 12, m and the associated NoiseStatus flag. If, in step (2004), the NoiseStatus flag indicates a noisy auscultatory sound sensor 12, m, then, in step (2006), the noise counter N_(NOISY) is incremented, and, in step (2008), the corresponding element of the status array Status[m, k] for the associated auscultatory sound sensor 12, m and breath-held segment k is updated to activate an associate Noisy flag, so as to indicate the associated auscultatory sound sensor 12, m being noisy for the associated breath-held segment k. Then, in step (2010), if the value of the noise counter N_(NOISY) is greater than 1, then, step (2012), the corresponding elements of the status array Status[m, k] for each of the auscultatory sound sensors 12, m is updated to activate the Ignore flag, so as to provide for ignoring each of the auscultatory sound sensors 12, m for the associated breath-held segment k. Then, or otherwise from step (2010), in step (2014), if the Noisy flag of the status array Status[m, k] is activated for at least N_(FAIL) breath-held segments k for the associated auscultatory sound sensor 12, m, then, in step (2016), status array Status[m] for the associated auscultatory sound sensor 12, m is updated to activate the Bad flag for the associated auscultatory sound sensor 12, m, so as to indicate that the associated auscultatory sound sensor 12, m is bad. Then, or otherwise from either step (2004) or step (2014), results-logging process 2000 terminates by returning to the noise detection process 1700.

More particularly, referring again to FIG. 17, in step (1720), if all of the pairs of adjacent auscultatory sound sensors 12 have not been processed, then, in step (1722), the pair pointer p is incremented, and the noise detection process 1700 is repeated for the next pair of auscultatory sound sensors 12, m1[p], m2[p], beginning with step (1706) for the same breath-held segment k. Otherwise, from step (1720), if in step (1724), additional segments of breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]] remain to be processed, then, in step (1726), the breath-held-segment pointer k is incremented, and the noise detection process 1700 is repeated beginning with step (1704) for the next segment of breath-held sampled auscultatory sound data S^(m)[i₀ [k]: i₁ [k]]. Otherwise, from step (1724), the noise detection process 1700 terminates with step (1728).

Referring again to FIG. 1, in accordance with one set of embodiments, the results from the docking system 27 may be transferred to a server computer system 56, for example, for subsequent transfer to an external data storage or processing system 58, for example, to provide for either viewing or analyzing the results at a remote location. Referring again to FIGS. 2 and 4, in one set of embodiments, the composite set of blocks of breath-held auscultatory-sound-sensor time-series data S are screened prior to analysis by the associated Data Analysis Application (DAA) 54, for example, by first segmenting the set of blocks of breath-held auscultatory-sound-sensor time-series data S by heart cycle with an associated segmentation process 60, and then validating the associated heart cycle data with an associated heart cycle validation process 62, for example, to provide for additional screening for heart-phase associated noise.

While specific embodiments have been described in detail in the foregoing detailed description and illustrated in the accompanying drawings, those with ordinary skill in the art will appreciate that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. It should be understood, that any reference herein to the term “or” is intended to mean an “inclusive or” or what is also known as a “logical OR”, wherein when used as a logic statement, the expression “A or B” is true if either A or B is true, or if both A and B are true, and when used as a list of elements, the expression “A, B or C” is intended to include all combinations of the elements recited in the expression, for example, any of the elements selected from the group consisting of A, B, C, (A, B), (A, C), (B, C), and (A, B, C); and so on if additional elements are listed. Furthermore, it should also be understood that the indefinite articles “a” or “an”, and the corresponding associated definite articles “the’ or “said”, are each intended to mean one or more unless otherwise stated, implied, or physically impossible. Yet further, it should be understood that the expressions “at least one of A and B, etc.”, “at least one of A or B, etc.”, “selected from A and B, etc.” and “selected from A or B, etc.” are each intended to mean either any recited element individually or any combination of two or more elements, for example, any of the elements from the group consisting of “A”, “B”, and “A AND B together”, etc. Yet further, it should be understood that the expressions “one of A and B, etc.” and “one of A or B, etc.” are each intended to mean any of the recited elements individually alone, for example, either A alone or B alone, etc., but not A AND B together. Furthermore, it should also be understood that unless indicated otherwise or unless physically impossible, that the above-described embodiments and aspects can be used in combination with one another and are not mutually exclusive. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the invention, which is to be given the full breadth of any claims that are supportable by the specification and drawings, and any and all equivalents thereof. 

What is claimed is:
 1. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor, comprising: a. receiving at least one first block of auscultatory sound data generated by a corresponding first auscultatory sound-or-vibration sensor of a pair of at least one pair of adjacent auscultatory sound-or-vibration sensors operatively coupled to a thorax of a test subject; b. receiving a corresponding at least one second block of auscultatory sound data generated by a corresponding second auscultatory sound-or-vibration sensor of said pair of said at least one pair of adjacent auscultatory sound-or-vibration sensors operatively coupled to said thorax of said test subject, wherein said at least one first block of auscultatory sound data and said corresponding at least one second block of auscultatory sound data are concurrently generated; c. generating a corresponding at least one first frequency spectrum of said at least one first block of auscultatory sound data; d. generating a corresponding at least one second frequency spectrum of said at least one second block of auscultatory sound data; e. generating a corresponding at least one noise filter array responsive to a cross-correlation of said corresponding at least one first frequency spectrum with said corresponding at least one second frequency spectrum; f. generating at least one first time-series array of noise data responsive to an inverse frequency transform of a product of said corresponding at least one first frequency spectrum with said corresponding at least one noise filter array; g. generating at least one first average noise-power frequency spectrum of said at least one first time-series array of noise data, wherein said at least one first average noise-power frequency spectrum of said at least one first time-series array of noise data is responsive to a plurality of first noise-power frequency spectra associated with a plurality of windows of said at least one first time-series array of noise data; h. determining at least one first noise power within at least one range of frequencies of said at least one first average noise-power frequency spectrum; and i. comparing said at least one first noise power with a corresponding at least one threshold to determine whether or not said first auscultatory sound-or-vibration sensor is excessively noisy.
 2. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein said at least one pair of adjacent auscultatory sound-or-vibration sensors comprises a plurality of pairs of adjacent auscultatory sound-or-vibration sensors, and each pair of said plurality of pairs of adjacent auscultatory sound-or-vibration sensors is processed in accordance with steps a though i of claim
 1. 3. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein each said pair of said at least one pair of adjacent auscultatory sound-or-vibration sensors is laterally adjacent to one another on said thorax of said test subject.
 4. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein said at least one first block of auscultatory sound data comprises a plurality of first blocks of auscultatory sound data, said at least one second block of auscultatory sound data comprises a plurality of second blocks of auscultatory sound data, each of said corresponding at least one first frequency spectrum, said corresponding at least one second frequency spectrum, said corresponding at least one noise filter array, said at least one first time-series array of noise data, said at least one first average noise-power frequency spectrum, and said at least one first noise power, are in block correspondence with one another and in block correspondence with each of said at least one first block of auscultatory sound data and said at least one second block of auscultatory sound data.
 5. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 4, A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, further comprising indicating that said first auscultatory sound-or-vibration sensor is defective if a number of instances, when said first auscultatory sound-or-vibration sensor is determined to be excessively noisy for different blocks of said plurality of first blocks, exceeds a threshold.
 6. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein said at least one first frequency spectrum comprises a corresponding at least one first array of data, different elements of each said first array of data are associated with different frequency components of said at least one first frequency spectrum, said at least one second frequency spectrum comprises a corresponding at least one second array of data, and different elements of each said second array of data are associated with different frequency components of said at least one second frequency spectrum.
 7. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 6, wherein each said at least one first frequency spectrum is responsive to a corresponding Fast Fourier Transform of a corresponding block of said at least one first block of auscultatory sound data, and each said at least one second frequency spectrum is responsive to a corresponding Fast Fourier Transform of a corresponding block of said at least one second block of auscultatory sound data in temporal correspondence with said corresponding block of said at least one first block of auscultatory sound data.
 8. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein said inverse frequency transform comprises an inverse Fast Fourier Transform.
 9. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein said plurality of windows of said at least one first time-series array of noise data overlap one another.
 10. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 9, wherein said plurality of windows of said at least one first time-series array of noise data overlap one another by a half window.
 11. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein each of said plurality of first noise-power frequency spectra associated with said plurality of windows of said at least one first time-series array of noise data is responsive to a Fast Fourier Transform of said at least one first time-series array of noise data within a corresponding window of said plurality of windows.
 12. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, wherein said at least one range of frequencies comprises a plurality of non-overlapping ranges of frequencies, and said at least one threshold comprises a corresponding plurality of thresholds in correspondence with said plurality of non-overlapping ranges of frequencies.
 13. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 1, further comprising: a. generating at least one second time-series array of noise data responsive to an inverse frequency transform of a product of said corresponding at least one second frequency spectrum with said corresponding at least one noise filter array; b. generating at least one second average noise-power frequency spectrum of said at least one second time-series array of noise data, wherein said at least one second average noise-power frequency spectrum of said at least one second time-series array of noise data is responsive to a plurality of second noise-power frequency spectra associated with a plurality of windows of said at least one second time-series array of noise data; c. determining at least one second noise power within said at least one range of frequencies of said at least one second average noise-power frequency spectrum; and d. comparing said at least one second noise power with said corresponding at least one threshold to determine whether or not said second auscultatory sound-or-vibration sensor is excessively noisy.
 14. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 13, wherein said inverse frequency transform comprises an inverse Fast Fourier Transform.
 15. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 13, wherein said plurality of windows of said at least one second time-series array of noise data overlap one another.
 16. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 13, wherein said plurality of windows of said at least one second time-series array of noise data overlap one another by a half window.
 17. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 13, wherein each of said plurality of second noise-power frequency spectra associated with a plurality of windows of said at least one second time-series array of noise data is responsive to a Fast Fourier Transform of said at least one second time-series array of noise data within a corresponding window of said plurality of windows.
 18. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 13, wherein said at least one first block of auscultatory sound data comprises a plurality of first blocks of auscultatory sound data, said at least one second block of auscultatory sound data comprises a plurality of second blocks of auscultatory sound data, each of said corresponding at least one first frequency spectrum, said corresponding at least one second frequency spectrum, said corresponding at least one noise filter array, said at least one second time-series array of noise data, said at least one second average noise-power frequency spectrum, and said at least one second noise power, are in block correspondence with one another and in block correspondence with each of said at least one first block of auscultatory sound data and said at least one second block of auscultatory sound data.
 19. A method of detecting noise in an auscultatory sound signal from an auscultatory sound-or-vibration sensor as recited in claim 18, further comprising causing said auscultatory sound data generated by every auscultatory sound-or-vibration sensor of said at least one pair of adjacent auscultatory sound-or-vibration sensors to be ignored for a period of time corresponding to said at least one first block of auscultatory sound data if a number of instances, when any said first or second auscultatory sound-or-vibration sensor of said at least one pair of adjacent auscultatory sound-or-vibration sensors is determined to be excessively noisy, exceeds a threshold. 